Systems, media, and methods applying machine learning to telematics data to generate vehicle fingerprint

ABSTRACT

Described herein are systems and methods for applying machine learning to telematics data to generate a unique vehicle fingerprint by periodically receiving telematics data generated at a plurality of sensors of a vehicle; standardizing the telematics data; aggregating the standardized telematics data; applying a trained machine learning model to embed the aggregated telematics data into a low-dimensional state; and generating a unique vehicle fingerprint, the vehicle fingerprint comprising a static component, a dynamic component, or both a static component and a dynamic component; including iterative repetition to update the dynamic component of the vehicle fingerprint.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Application No. 16/791,553,filed Feb. 14, 2020, which claims the benefit of U.S. ProvisionalApplication No. 62/932,008, filed Nov. 7, 2019, and U.S. ProvisionalApplication No. 62/807,508, filed Feb. 19, 2019, each of which is herebyincorporated by reference in its entirety.

BACKGROUND

The way in which an operator operates a vehicle (e.g., drive anautomobile) is unique. A vehicle may include sensors and on-boardhardware to harvest and process data collected while in use.Analogously, the way in which a vehicle (e.g., an automobile) operatesis also unique.

SUMMARY

Telematics data can be employed to generate a unique “fingerprint” (or“signature”) for each operator of the vehicle, which can be employed toidentify an operator, or to discover characteristic insights about theoperator, while he or she is operating the vehicle. Analogously, thistelematics data can also be employed to generate a unique fingerprint ofthe vehicle itself, which can be employed to determine the health of thevehicle and generate insights about future vehicle component and systemsfailures. One problem with fingerprinting an operator of a vehicle (orthe vehicle itself) is inferring a unique “fingerprint” for eachoperator, to better understand who is operating the vehicle, how well heor she is operating the vehicle, whether the operator is tired ordistracted, and how their operation of the vehicle evolves over time.This “fingerprint” (or “signature”) may be generated using telematicsdata that is collected while the vehicle is being operated. In someembodiments, the telematics data is accessed through the vehicle’sController Area Network (CAN) bus.

Once generated, a fingerprint for an operator or the vehicle isextremely valuable as it can be used for a variety of purposes tounderstand both the operator and the context that the operator isoperating the vehicle in. For example, an operator fingerprint can beused to verify that a particular operator is operating the vehicle toauthenticate a credit card payment using an in-vehicle payment system.As another example, an operator fingerprint can be used to provideinterpretable risk profiling, which produces actionable insights on anoperator’s ability. Such insights can be used for, among other things,usage-based insurance purposes. As further example, a vehiclefingerprint can be used to predict when a specific vehicle component orsystem is likely to malfunction or fail. Moreover, evaluating thetemporal evolutions of fingerprints opens up entirely new use cases. Forexample, when combined with a real-time alert system, fingerprints canbe used to detect distracted operators and alert them while they are insuch a potentially dangerous scenario as well provide predictivemaintenance recommendations.

Determining an operator’s or vehicle’s fingerprint is a challengebecause it must satisfy a number of requirements, while also aggregatinglarge amounts of telematics data into, for example, a low-dimensionalrepresentation. As an exemplary requirement, an operator fingerprintmust be able to distinguish unique characteristics among differentoperators or vehicles to effectively identify an individual operatorbased on their operating patterns. A fingerprint, as another examplerequirement, must evolve over time based on context (e.g., showing thata given driver is “tired,” “stuck in traffic,” and so forth). Afingerprint, as yet another example requirement, must be independent ofroad type, weather, and other exogenous factors. Other examplerequirements for a fingerprint include that it must be able to capturesimilarity between operators such that similar operators (e.g.,aggressive, distracted, cautious, and so forth) have similarfingerprints and that spatial locations of the fingerprints need to beinterpretable along multiple dimensions of interest (e.g., risk, style,driving conditions, and so forth). Analogously, determining a vehicle’sfingerprint is also a challenge due to the need to satisfy a number ofrequirements while also aggregating large amounts of telematics datainto, for example, a low-dimensional representation. Currently, notechniques exist that can accomplish all these goals/requirements.

The described is compact and low dimensional and thus easy to transmitwhen, for example, bandwidth is a constraint. Moreover, the describedsystem is interpretable across various applications, and can also beemployed for several purposes at once (e.g., credit card authorization,distracted driving detection, risk profiling, predictive maintenance,and so forth). The described system is transferrable as operatorsoperate (e.g., drive) different vehicles and travel to differentlocations. Therefore, a determined operator fingerprint can “follow” anoperator across various systems and over many years. Similarly, adetermined vehicle fingerprint can be used to follow the vehicle acrossvarious operators and over many years.

In one aspect, disclosed herein are computer-implemented systemscomprising: a digital processing device comprising: at least oneprocessor, an operating system configured to perform executableinstructions, a memory, and a computer program including instructionsexecutable by the digital processing device to create an applicationapplying machine learning to telematics data to generate a unique driverfingerprint for an individual, the application comprising: a softwaremodule periodically receiving telematics data generated at a pluralityof sensors of a vehicle; a software module standardizing the telematicsdata; a software module aggregating the standardized telematics data; asoftware module applying a trained machine learning model to embed theaggregated telematics data into a low-dimensional state; and a softwaremodule generating a unique driver fingerprint for the individual, thedriver fingerprint comprising a static component and/or a dynamiccomponent; wherein some or all of the functions are iteratively repeatedto update the dynamic component of the driver fingerprint. In someembodiments, the telematics data originates at a plurality of vehiclesensors connected to the vehicle’s CAN bus. In some embodiments, thetelematics data is transmitted wirelessly via the vehicle’s connectivitymodule. In some embodiments, the telematics data comprises vehicle data.In further embodiments, the vehicle data comprises one or more of:travel speed, wheel speed, acceleration, orientation, engine revolutionsper minute (RPM), engine temperature, coolant temperature, oiltemperature, current gear, battery voltage, suspension activity, climatecontrol system settings, window positions, door statuses, mirrorpositions, internal air temperature, tire pressures, seat belt tension,tire pressure, passenger occupancy, radar status, and personalizationsettings. In some embodiments, the telematics data comprisesenvironmental data. In further embodiments, the environmental datacomprises one or more of: location, altitude, external air temperature,external humidity, precipitation, road type, light, and road condition.In some embodiments, the telematics data comprises driver data. Infurther embodiments, the driver data comprises one or more of: steeringwheel position, steering wheel velocity, brake pedal position, brakingforce, gas pedal position, shifting, internal lighting use, headlightuse, turn signal use, mirror adjustments, window adjustments, climatecontrol system use, entertainment system use, and seat belt use. In someembodiments, the telematics data comprises demographics data. In furtherembodiments, the demographics data comprises one or more of: age,gender, religion, race, income, education, and employment. In someembodiments, at least some of the telematics data is sequential timeseries data. In various embodiments, the telematics data is received atleast every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, includingincrements therein. In further embodiments, the telematics data isreceived substantially continuously. In some embodiments, the machinelearning model comprises a neural network. In further embodiments, theneural network is a plurality of stacked recurrent neural networks. Instill further embodiments, the neural network comprises a plurality ofrecurrent neural networks and a fully connected layer. In variousembodiments, some or all of the functions are iteratively repeated toupdate the dynamic component of the driver fingerprint at least every 15minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15seconds, 10 seconds, 5 seconds, or 1 second, including incrementstherein. In further embodiments, some or all of the functions areiteratively repeated to update the dynamic component of the driverfingerprint substantially continuously. In various embodiments, thedriver fingerprint comprises one or more of: a level of aggression, alevel of distraction, a level of impairment, a level of driving risk, alevel of driving skill, a driving style; and a driver identity. In someembodiments, the application further comprises a software moduleidentifying driver fingerprints, from among a plurality of driverfingerprints, which are similar to each other. In further embodiments,the similarity is measured by a calculated similarity score. In variousembodiments, the application further comprises a software moduleutilizing the driver fingerprint to perform one or more of: authenticatethe individual in a payment system, determine an insurance pricingfactor for the individual, detect changes in driving behavior of theindividual, and personalize vehicle settings for the individual.

In another aspect, disclosed herein are computer-implemented methods ofgenerating a unique driver fingerprint for an individual comprising:periodically collecting, by a computer, telematics data generated at aplurality of sensors of a vehicle; standardizing, by the computer, thetelematics data; training, at a compute cluster (such as a GPU cluster),a machine learning model to embed the aggregated telematics data into alow-dimensional state; applying, by the computer or a vehicle, thetrained machine learning model to embed the aggregated telematics datainto a low-dimensional state; generating, by the computer or thevehicle, a unique driver fingerprint, the driver fingerprint comprisinga static component and/or a dynamic component; and iteratively repeatingsome or all of the steps to update the dynamic component of the driverfingerprint. In some embodiments, the method further comprises: saving,by the computer or the vehicle, weights generated by the trained machineleaning model; and inferring, by the computer or the vehicle, a uniquedriver fingerprint for the individual based on the weights for noveltelematics data generated at a plurality of sensors of the vehicle. Insome embodiments, the telematics data originates at a plurality ofvehicle sensors connected to the vehicle’s CAN bus. In some embodiments,the telematics data is transmitted wirelessly via the vehicle’sconnectivity module. In some embodiments, the telematics data comprisesvehicle data. In further embodiments, the vehicle data comprises one ormore of: travel speed, wheel speed, acceleration, orientation, engineRPM, engine temperature, coolant temperature, oil temperature, currentgear, battery voltage, suspension activity, climate control systemsettings, window positions, door statuses, mirror positions, internalair temperature, tire pressures, seat belt tension, tire pressure,passenger occupancy, radar status, and personalization settings. In someembodiments, the telematics data comprises environmental data. Infurther embodiments, the environmental data comprises one or more of:location, altitude, external air temperature, external humidity,precipitation, road type, light, and road condition. In someembodiments, the telematics data comprises driver data. In furtherembodiments, the driver data comprises one or more of: steering wheelposition, steering wheel velocity, brake pedal position, braking force,gas pedal position, shifting, internal lighting use, headlight use, turnsignal use, mirror adjustments, window adjustments, climate controlsystem use, entertainment system use, and seat belt use. In someembodiments, the telematics data comprises demographics data. In furtherembodiments, the demographics data comprises one or more of: age,gender, religion, race, income, education, and employment. In someembodiments, at least some of the telematics data is sequential timeseries data. In various embodiments, the collecting the telematics dataoccurs at least every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second,including increments therein. In further embodiments, the collecting thetelematics data occurs substantially continuously. In some embodiments,the machine learning model comprises a neural network. In furtherembodiments, the neural network is a plurality of stacked recurrentneural networks. In still further embodiments, the neural networkcomprises a plurality of recurrent neural networks and a fully connectedlayer. In various embodiments, some or all of the steps are iterativelyrepeated to update the dynamic component of the driver fingerprint atleast every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, includingincrements therein. In further embodiments, some or all of the steps areiteratively repeated to update the dynamic component of the driverfingerprint substantially continuously. In various embodiments, thedriver fingerprint comprises one or more of: a level of aggression, alevel of distraction, a level of impairment, a level of driving risk, alevel of driving skill, a driving style, and a driver identity. In someembodiments, the method further comprises identifying, by the computer,two or more driver fingerprints, from among a plurality of driverfingerprints, which are similar to each other. In further embodiments,the similarity is measured by a calculated similarity score. In variousembodiments, the method further comprises utilizing, by the computer orthe vehicle, the driver fingerprint to perform one or more of:authenticate the individual in a payment system, determine an insurancepricing factor for the individual, detect changes in driving behavior ofthe individual, and personalize vehicle settings for the individual.

In yet another aspect, disclosed herein are systems for applying machinelearning to telematics data to generate a unique driver fingerprint foran individual comprising: at least one server processor configured toperform at least the following: periodically receive telematics datagenerated at a plurality of sensors of a vehicle; standardize thetelematics data; aggregate the standardized telematics data; apply atrained machine learning model to embed the aggregated telematics datainto a low-dimensional state; save weights generated by the trainedmachine leaning model; transmit the saved weights to the vehicle; anditeratively repeating some or all of the functions to update thetransmitted weights; and at least one vehicle processor configured toperform at least the following: receive the transmitted weights; andinfer a unique driver fingerprint for the individual based on thetransmitted weights for novel telematics data generated at a pluralityof sensors of the vehicle, the driver fingerprint comprising a staticcomponent and/or a dynamic component. In some embodiments, thetelematics data originates at a plurality of vehicle sensors connectedto the vehicle’s CAN bus. In some embodiments, the telematics data istransmitted wirelessly via the vehicle’s connectivity module. In someembodiments, the telematics data comprises vehicle data. In furtherembodiments, the vehicle data comprises one or more of: travel speed,wheel speed, acceleration, orientation, engine RPM, engine temperature,coolant temperature, oil temperature, current gear, battery voltage,suspension activity, climate control system settings, window positions,door statuses, mirror positions, internal air temperature, tirepressures, seat belt tension, tire pressure, passenger occupancy, radarstatus, and personalization settings. In some embodiments, thetelematics data comprises environmental data. In further embodiments,the environmental data comprises one or more of: location, altitude,external air temperature, external humidity, precipitation, road type,light, and road condition. In some embodiments, the telematics datacomprises driver data. In further embodiments, the driver data comprisesone or more of: steering wheel position, steering wheel velocity, brakepedal position, braking force, gas pedal position, shifting, internallighting use, headlight use, turn signal use, mirror adjustments, windowadjustments, climate control system use, entertainment system use, andseat belt use. In some embodiments, the telematics data comprisesdemographics data. In further embodiments, the demographics datacomprises one or more of: age, gender, religion, race, income,education, and employment. In some embodiments, at least some of thetelematics data is sequential time series data. In various embodiments,the telematics data is received at the at least one server processor atleast every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, includingincrements therein. In further embodiments, the telematics data isreceived at the at least one server processor substantiallycontinuously. In some embodiments, the machine learning model comprisesa neural network. In further embodiments, the neural network is aplurality of stacked recurrent neural networks. In still furtherembodiments, the neural network comprises a plurality of recurrentneural networks and a fully connected layer. In various embodiments, themodel weights are iteratively updated at least every 15 minutes, 10minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10seconds, 5 seconds, or 1 second, including increments therein. Infurther embodiments, the model weights are iteratively substantiallycontinuously. In various embodiments, the driver fingerprint comprisesone or more of: a level of aggression, a level of distraction, a levelof impairment, a level of driving risk, a level of driving skill, adriving style, and a driver identity. In some embodiments, the at leastone vehicle processor is further configured to identify driverfingerprints, from among a plurality of driver fingerprints, that aresimilar to each other. In further embodiments, the similarity ismeasured by a calculated similarity score. In various embodiments, theat least one vehicle processor is further configured to utilize thedriver fingerprint to perform one or more of: authenticate theindividual in a payment system, determine an insurance pricing factorfor the individual, detect changes in driving behavior of theindividual, and personalize vehicle settings for the individual.

In one aspect, disclosed herein are computer-implemented systemscomprising: a digital processing device comprising: at least oneprocessor, an operating system configured to perform executableinstructions, a memory, and a computer program including instructionsexecutable by the digital processing device to create an applicationapplying machine learning to telematics data to generate a uniquevehicle fingerprint for a specific vehicle, the application comprising:a software module periodically receiving telematics data generated at aplurality of sensors of a vehicle; a software module standardizing thetelematics data; a software module aggregating the standardizedtelematics data; a software module applying a trained machine learningmodel to embed the aggregated telematics data into a low-dimensionalstate; and a software module generating a unique vehicle fingerprint forthe specific vehicle, the vehicle fingerprint comprising a staticcomponent, a dynamic component, or both a static component and a dynamiccomponent; wherein some or all of the functions are iteratively repeatedto update the dynamic component of the vehicle fingerprint. In someembodiments, the telematics data originates at a plurality of vehiclesensors connected to the vehicle’s controller area network (CAN) bus. Insome embodiments, the telematics data is transmitted wirelessly via thevehicle’s connectivity module. In some embodiments, the telematics datacomprises vehicle data. In further embodiments, the vehicle datacomprises one or more of: travel speed, wheel speed, acceleration,orientation, engine revolutions per minute (RPM), engine temperature,coolant temperature, oil temperature, current gear, battery voltage,suspension activity, climate control system settings, window positions,door statuses, mirror positions, internal air temperature, tirepressures, seat belt tension, tire pressure, passenger occupancy, radarstatus, diagnostic trouble codes, historical maintenance triggers, andpersonalization settings. In some embodiments, the telematics datacomprises environmental data. In further embodiments, the environmentaldata comprises one or more of: location, altitude, external airtemperature, external humidity, precipitation, road type, light, androad condition. In some embodiments, the telematics data comprisesdriver data. In further embodiments, the driver data comprises one ormore of: steering wheel position, steering wheel velocity, brake pedalposition, braking force, gas pedal position, shifting, internal lightinguse, headlight use, turn signal use, mirror adjustments, windowadjustments, climate control system use, entertainment system use, andseat belt use. In some embodiments, at least some of the telematics datais sequential time series data. In various embodiments, the telematicsdata is received at least every 15 minutes, 10 minutes, 5 minutes, 1minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1second. In a particular embodiment, the telematics data is receivedsubstantially continuously. In some embodiments, the machine learningmodel comprises a neural network. In further embodiments, the neuralnetwork is a plurality of stacked recurrent neural networks. In stillfurther embodiments, the neural network comprises a plurality ofrecurrent neural networks and a fully connected layer. In variousembodiments, some or all of the functions are iteratively repeated toupdate the dynamic component of the vehicle fingerprint at least every15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15seconds, 10 seconds, 5 seconds, or 1 second. In a particular embodiment,some or all of the functions are iteratively repeated to update thedynamic component of the vehicle fingerprint substantially continuously.In some embodiments, the vehicle fingerprint comprises a componentmalfunction or vehicle system failure risk. In some embodiments, thevehicle fingerprint comprises a system-aggregated malfunction or failurerisk. In some embodiments, the application further comprises a softwaremodule identifying vehicle fingerprints, from among a plurality ofvehicle fingerprints, which are similar to each other. In furtherembodiments, the similarity is measured by a calculated similarityscore. In some embodiments, the application further comprises a softwaremodule utilizing the vehicle fingerprint to predict futurecomponent-specific or vehicle-system-specific malfunctions or failures.In some embodiments, the application further comprises a software moduleutilizing the vehicle fingerprint to predict future system-aggregatedmalfunctions or failures. In some embodiments, the application furthercomprises a software module utilizing the vehicle fingerprint toidentify and group vehicles based on common component-specific orsystem-aggregated malfunction or failure histories.

In another aspect, disclosed herein are computer-implemented methods ofgenerating a unique vehicle fingerprint for a specific vehiclecomprising: periodically collecting, by a computer, telematics datagenerated at a plurality of sensors of a vehicle; standardizing, by thecomputer, the telematics data; training, at a computer cluster, amachine learning model to embed the aggregated telematics data into alow-dimensional state; applying, by the computer or a vehicle, thetrained machine learning model to embed the aggregated telematics datainto a low-dimensional state; generating, by the computer or thevehicle, a unique vehicle fingerprint, the vehicle fingerprintcomprising a static component, a dynamic component, or both a staticcomponent and a dynamic component; and iteratively repeating some or allof the steps to update the dynamic component of the vehicle fingerprint.In some embodiments, the method further comprises: saving, by thecomputer or the vehicle, weights generated by the trained machineleaning model; and inferring, by the computer or the vehicle, a uniquevehicle fingerprint for the specific vehicle based on the weights fornovel telematics data generated at a plurality of sensors of thevehicle. In some embodiments, the telematics data originates at aplurality of vehicle sensors connected to the vehicle’s controller areanetwork (CAN) bus. In some embodiments, the telematics data istransmitted wirelessly via the vehicle’s connectivity module. In someembodiments, the telematics data comprises vehicle data. In furtherembodiments, the vehicle data comprises one or more of: travel speed,wheel speed, acceleration, orientation, engine RPM, engine temperature,coolant temperature, oil temperature, current gear, battery voltage,suspension activity, climate control system settings, window positions,door statuses, mirror positions, internal air temperature, tirepressures, seat belt tension, tire pressure, passenger occupancy, radarstatus, diagnostic trouble codes, historical maintenance triggers, andpersonalization settings. In some embodiments, the telematics datacomprises environmental data. In further embodiments, the environmentaldata comprises one or more of: location, altitude, external airtemperature, external humidity, precipitation, road type, light, androad condition. In some embodiments, the telematics data comprisesdriver data. In further embodiments, the driver data comprises one ormore of: steering wheel position, steering wheel velocity, brake pedalposition, braking force, gas pedal position, shifting, internal lightinguse, headlight use, turn signal use, mirror adjustments, windowadjustments, climate control system use, entertainment system use, andseat belt use. In some embodiments, at least some of the telematics datais sequential time series data. In various embodiments, the collectingthe telematics data occurs at least every 15 minutes, 10 minutes, 5minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5seconds, or 1 second. In a particular embodiment, the collecting thetelematics data occurs substantially continuously. In some embodiments,the machine learning model comprises a neural network. In furtherembodiments, the neural network is a plurality of stacked recurrentneural networks. In still further embodiments, the neural networkcomprises a plurality of recurrent neural networks and a fully connectedlayer. In some embodiments, some or all of the steps are iterativelyrepeated to update the dynamic component of the vehicle fingerprint atleast every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second. In a particularembodiment, some or all of the steps are iteratively repeated to updatethe dynamic component of the vehicle fingerprint substantiallycontinuously. In some embodiments, the vehicle fingerprint comprises acomponent malfunction or vehicle system failure risk. In someembodiments, the vehicle fingerprint comprises a system-aggregatedmalfunction or failure risk. In some embodiments, the method furthercomprises identifying, by the computer, two or more vehiclefingerprints, from among a plurality of vehicle fingerprints, which aresimilar to each other. In further embodiments, the similarity ismeasured by a calculated similarity score. In some embodiments, themethod further comprises utilizing, by the computer or the vehicle, thevehicle fingerprint to predict future component-specific orvehicle-system-specific malfunctions or failures. In some embodiments,the method further comprises utilizing, by the computer or the vehicle,the vehicle fingerprint to predict future system-aggregated malfunctionsor failures. In some embodiments, the method further comprisesutilizing, by the computer or the vehicle, the vehicle fingerprint toidentify and group vehicles based on common component-specific orsystem-aggregated malfunction or failure histories.

In yet another aspect, disclosed herein are systems for applying machinelearning to telematics data to generate a unique vehicle fingerprint fora specific vehicle comprising: at least one server processor configuredto perform at least the following: periodically receive telematics datagenerated at a plurality of sensors of a vehicle; standardize thetelematics data; aggregate the standardized telematics data; apply atrained machine learning model to embed the aggregated telematics datainto a low-dimensional state; save weights generated by the trainedmachine leaning model; transmit the saved weights to the vehicle; anditeratively repeating some or all of the functions to update thetransmitted weights; and at least one vehicle processor configured toperform at least the following: receive the transmitted weights; andinfer a unique vehicle fingerprint for the specific vehicle based on thetransmitted weights for novel telematics data generated at a pluralityof sensors of the vehicle, the vehicle fingerprint comprising a staticcomponent, a dynamic component, or both a static component and a dynamiccomponent. In some embodiments, the telematics data originates at aplurality of vehicle sensors connected to the vehicle’s controller areanetwork (CAN) bus. In some embodiments, the telematics data istransmitted wirelessly via the vehicle’s connectivity module. In someembodiments, the telematics data comprises vehicle data. In furtherembodiments, the vehicle data comprises one or more of: travel speed,wheel speed, acceleration, orientation, engine RPM, engine temperature,coolant temperature, oil temperature, current gear, battery voltage,suspension activity, climate control system settings, window positions,door statuses, mirror positions, internal air temperature, tirepressures, seat belt tension, tire pressure, passenger occupancy, radarstatus, diagnostic trouble codes, historical maintenance triggers, andpersonalization settings. In some embodiments, the telematics datacomprises environmental data. In further embodiments, the environmentaldata comprises one or more of: location, altitude, external airtemperature, external humidity, precipitation, road type, light, androad condition. In some embodiments, the telematics data comprisesdriver data. In further embodiments, the driver data comprises one ormore of: steering wheel position, steering wheel velocity, brake pedalposition, braking force, gas pedal position, shifting, internal lightinguse, headlight use, turn signal use, mirror adjustments, windowadjustments, climate control system use, entertainment system use, andseat belt use. In some embodiments, at least some of the telematics datais sequential time series data. In various embodiments, the telematicsdata is received at the at least one server processor at least every 15minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds, 30 seconds, 15seconds, 10 seconds, 5 seconds, or 1 second. In a particular embodiment,the telematics data is received at the at least one server processorsubstantially continuously. In some embodiments, the machine learningmodel comprises a neural network. In further embodiments, the neuralnetwork is a plurality of stacked recurrent neural networks. In stillfurther embodiments, the neural network comprises a plurality ofrecurrent neural networks and a fully connected layer. In variousembodiments, some or all of the functions are iteratively repeated toupdate the weights at least every 15 minutes, 10 minutes, 5 minutes, 1minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1second. In a particular embodiment, some or all of the functions areiteratively repeated to update the weights substantially continuously.In some embodiments, the vehicle fingerprint comprises a componentmalfunction or vehicle system failure risk. In some embodiments, thevehicle fingerprint comprises a system-aggregated malfunction or failurerisk. In some embodiments, the at least one vehicle processor is furtherconfigured to identify vehicle fingerprints, from among a plurality ofvehicle fingerprints, that are similar to each other. In furtherembodiments, the similarity is measured by a calculated similarityscore. In some embodiments, the at least one vehicle processor isfurther configured to utilize the vehicle fingerprint to predict futurecomponent-specific or vehicle-system-specific malfunctions or failures.In some embodiments, the at least one vehicle processor is furtherconfigured to utilize the vehicle fingerprint to predict futuresystem-aggregated malfunctions or failures. In some embodiments, the atleast one vehicle processor is further configured to utilize the vehiclefingerprint to identify and group vehicles based on commoncomponent-specific or system-aggregated malfunction or failurehistories.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the features and advantages of the presentsubject matter will be obtained by reference to the following detaileddescription that sets forth illustrative embodiments and theaccompanying drawings of which:

FIG. 1 depicts a non-limiting example of a process for generating aunique driver fingerprint for an individual or vehicle fingerprint for aspecific vehicle based on telematics data as described herein;

FIG. 2 depicts a non-limiting example of a system for generating aunique driver fingerprint for an individual or vehicle fingerprint for aspecific vehicle by applying a machine learning model to telematics dataas described herein;

FIG. 3 depicts a non-limiting example of a system for generating aunique driver fingerprint for an individual or vehicle fingerprint for aspecific vehicle by applying recurrent neural networks to telematicsdata as described herein;

FIG. 4 depicts an example system to generate and update a fingerprintfor an operator of a vehicle or the vehicle itself;

FIG. 5 depicts an example environment that can be employed to executeimplementations of the present disclosure; and

FIG. 6 depicts a non-limiting example of a digital processing device; inthis case, a device with one or more CPUs, a memory, a communicationinterface, and a display.

DETAILED DESCRIPTION

Described herein, in certain embodiments, are computer-implementedsystems comprising: a digital processing device comprising: at least oneprocessor, an operating system configured to perform executableinstructions, a memory, and a computer program including instructionsexecutable by the digital processing device to create an applicationapplying machine learning to telematics data to generate a unique driverfingerprint for an individual, the application comprising: a softwaremodule periodically receiving telematics data generated at a pluralityof sensors of a vehicle; a software module standardizing the telematicsdata; a software module aggregating the standardized telematics data; asoftware module applying a trained machine learning model to embed theaggregated telematics data into a low-dimensional state; and a softwaremodule generating a unique driver fingerprint for the individual, thedriver fingerprint comprising a static component and/or a dynamiccomponent; wherein some or all of the functions are iteratively repeatedto update the dynamic component of the driver fingerprint.

Also described herein, in certain embodiments, are computer-implementedmethods of generating a unique driver fingerprint for an individualcomprising: periodically collecting, by a computer, telematics datagenerated at a plurality of sensors of a vehicle; standardizing, by thecomputer, the telematics data; training, at a compute cluster (such as aGPU cluster), a machine learning model to embed the aggregatedtelematics data into a low-dimensional state; applying, by the computeror a vehicle, the trained machine learning model to embed the aggregatedtelematics data into a low-dimensional state; generating, by thecomputer or the vehicle, a unique driver fingerprint, the driverfingerprint comprising a static component and/or a dynamic component;and iteratively repeating some or all of the steps to update the dynamiccomponent of the driver fingerprint.

Also described herein, in certain embodiments, are systems for applyingmachine learning to telematics data to generate a unique driverfingerprint for an individual comprising: at least one server processorconfigured to perform at least the following: periodically receivetelematics data generated at a plurality of sensors of a vehicle;standardize the telematics data; aggregate the standardized telematicsdata; apply a trained machine learning model to embed the aggregatedtelematics data into a low-dimensional state; save weights generated bythe trained machine leaning model; transmit the saved weights to thevehicle; and iteratively repeating some or all of the functions toupdate the transmitted weights; and at least one vehicle processorconfigured to perform at least the following: receive the transmittedweights; and infer a unique driver fingerprint for the individual basedon the transmitted weights for novel telematics data generated at aplurality of sensors of the vehicle, the driver fingerprint comprising astatic component and/or a dynamic component.

Described herein, in certain embodiments, are computer-implementedsystems comprising: a digital processing device comprising: at least oneprocessor, an operating system configured to perform executableinstructions, a memory, and a computer program including instructionsexecutable by the digital processing device to create an applicationapplying machine learning to telematics data to generate a uniquevehicle fingerprint for a specific vehicle, the application comprising:a software module periodically receiving telematics data generated at aplurality of sensors of a vehicle; a software module standardizing thetelematics data; a software module aggregating the standardizedtelematics data; a software module applying a trained machine learningmodel to embed the aggregated telematics data into a low-dimensionalstate; and a software module generating a unique vehicle fingerprint forthe specific vehicle, the vehicle fingerprint comprising a staticcomponent, a dynamic component, or both a static component and a dynamiccomponent; wherein some or all of the functions are iteratively repeatedto update the dynamic component of the vehicle fingerprint.

Also described herein, in certain embodiments, are computer-implementedmethods of generating a unique vehicle fingerprint for a specificvehicle comprising: periodically collecting, by a computer, telematicsdata generated at a plurality of sensors of a vehicle; standardizing, bythe computer, the telematics data; training, at a computer cluster, amachine learning model to embed the aggregated telematics data into alow-dimensional state; applying, by the computer or a vehicle, thetrained machine learning model to embed the aggregated telematics datainto a low-dimensional state; generating, by the computer or thevehicle, a unique vehicle fingerprint, the vehicle fingerprintcomprising a static component, a dynamic component, or both a staticcomponent and a dynamic component; and iteratively repeating some or allof the steps to update the dynamic component of the vehicle fingerprint.

Also described herein, in certain embodiments, are systems for applyingmachine learning to telematics data to generate a unique vehiclefingerprint for a specific vehicle comprising: at least one serverprocessor configured to perform at least the following: periodicallyreceive telematics data generated at a plurality of sensors of avehicle; standardize the telematics data; aggregate the standardizedtelematics data; apply a trained machine learning model to embed theaggregated telematics data into a low-dimensional state; save weightsgenerated by the trained machine leaning model; transmit the savedweights to the vehicle; and iteratively repeating some or all of thefunctions to update the transmitted weights; and at least one vehicleprocessor configured to perform at least the following: receive thetransmitted weights; and infer a unique vehicle fingerprint for thespecific vehicle based on the transmitted weights for novel telematicsdata generated at a plurality of sensors of the vehicle, the vehiclefingerprint comprising a static component, a dynamic component, or botha static component and a dynamic component.

Certain Definitions

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich the described subject matter belongs. As used in thisspecification and the appended claims, the singular forms “a,” “an,” and“the” include plural references unless the context clearly dictatesotherwise. Any reference to “or” herein is intended to encompass“and/or” unless otherwise stated.

Vehicles

In some embodiments, the platforms, systems, media, and methodsdescribed herein include a vehicle. The vehicle may be stationary,moving, or capable of movement. The vehicle may have an interior cabinwith space for an operator and one or more passengers. The vehicle maybe any suitable terrestrial vehicle, aerial vehicle, aquatic vehicle. Aterrestrial vehicle may be a motor vehicle or any other vehicle thatuses a source of energy, renewable or nonrenewable (e.g., solar,thermal, electrical, wind, petroleum, etc.), to move across or in closeproximity to the ground, such as, for example, within 1 meter, 2 meters,3 meters of the ground. For example, a terrestrial vehicle may include acar, bus, train, truck, bicycle, motorcycle, forklift, scooter, or anytransportation device for use on the ground. An aerial vehicle may be amotor vehicle or any other vehicle that uses a source of energy,renewable or nonrenewable, (solar, thermal, electrical, wind, petroleum,etc.) to move through the air or through space such as a plane,helicopter, or airship. An aquatic vehicle may be a motor vehicle or anyother vehicle that uses a source of energy, renewable or nonrenewable,(solar, thermal, electrical, wind, petroleum, etc.) to move across orthrough water, such as a boat, submarine, or the like.

In some embodiments, a vehicle employed within the described system mayinclude various sensor devices to collected telematics data. Such sensordevices may include, but are not limited to, accelerometers, gyroscopes,magnetometers, position systems, such as a Global Navigation SatelliteSystem (GNSS), barometers, speedometers, and so forth. These sensordevices may be mounted to any side of the vehicle, or to one or moresides of the vehicle, e.g., a front side, rear side, lateral side, topside, or bottom side. The front side of the vehicle may be the side thatis facing a general direction of travel of the vehicle; while a rear (orback) side may be the side that is not facing the general direction oftravel of the vehicle. The rear side of the vehicle may be opposite tothe front side. The front side of the vehicle may point toward a forwarddirection of travel of the vehicle. The rear side of the vehicle maypoint towards a rear direction of travel (e.g., reverse) of the vehicle.In some cases, the sensor devices may be mounted between two adjacentsides of a vehicle.

Telematics Data

In some contexts, the described system collects, stores, and processestelematics data from sensors placed within a vehicle or from sensorswithin or coupled to a mobile device, such as a smart phone, that iswithin a vehicle. Telematics data can measure the car’s environment(e.g., weather, location, altitude) as well as the operator’s actions(e.g., steering wheel position, gas and brake pedal press amount). Insome embodiments, the telematics data originates at a plurality ofvehicle sensors connected to the vehicle’s CAN bus. In some embodiments,the telematics data is transmitted wirelessly via the vehicle’sconnectivity module to a back end system to include a data store (seeFIG. 2 ). In some embodiments, the telematics data comprises vehicledata. Such vehicle data may include, for example, travel speed, wheelspeed, acceleration, orientation, engine RPM, engine temperature,coolant temperature, oil temperature, current gear, battery voltage,suspension activity, climate control system settings, window positions,door statuses, mirror positions, internal air temperature, tirepressures, seat belt tension, tire pressure, passenger occupancy, radarstatus, diagnostic trouble codes, historical maintenance triggers, andpersonalization settings. In some embodiments, the telematics datacomprises environmental data. Such environmental may include, forexample, location, altitude, external air temperature, externalhumidity, precipitation, road type, light, and road condition. In someembodiments, the telematics data comprises driver data. Such driver datamay include, for example, steering wheel position, steering wheelvelocity, brake pedal position, braking force, gas pedal position,shifting, internal lighting use, headlight use, turn signal use, mirroradjustments, window adjustments, climate control system use,entertainment system use, and seat belt use. In some embodiments, thetelematics data comprises demographics data. Such demographics data mayinclude age, gender, religion, race, income, education, and employment.In some embodiments, at least some of the telematics data is sequentialtime series data. In some embodiments, the telematics data is receivedat least every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45 seconds,30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second, includingincrements therein. In some embodiments, the telematics data is receivedsubstantially continuously

In some contexts, the described system employs mobile sensingtechnologies to collect, store, and process telematics data frombuilt-in or external sensors of a mobile device, such as a smartphone.In some embodiments, the described system collects telematics data whena vehicle in in motion, or has undergone a significant change inlocation from a previous known position. In such embodiments, thedescribed system may collect data from the sensors until the vehiclecome to rest for a set threshold amount of time.

In some embodiments, the collected telematics data is timestamped. Asequence of such timestamped telematics may be referred to as arecording. In some embodiments, telematics data is collectedcontinuously during a period; in this case, sequences of the collecteddata may be segmented according to a rule (e.g., sequences that areseparated by substantial periods of rest) to form multiple recordings.

In some embodiments, the collected telematics data is standardized. Forexample, the telematics data may be standardized across makes, models,and brands of vehicles.

Machine Learning Model

Machine learning includes the study of computer modeling of learningprocesses in their multiple manifestations. In general, learningprocesses include various aspects such as the acquisition of newdeclarative knowledge, the devilment of motor and cognitive skillsthrough instruction or practice, the organization of new knowledge intogeneral, effective representations, and the discovery of new facts andtheories through observation and experimentations.

In some embodiments, machine learning models are trained and employed toaid in determining a fingerprint for an operator of a vehicle.

Machine learning algorithms can be trained with, for example, telematicsdata to determine a fingerprint for an operator of a vehicle, which maybe subsequently employed within various decision-making processes. Insome embodiments, the machine learning models employ regressionmodelling, wherein relationships between predictor variables anddependent variables are determined and weighted. Examples of algorithmsemployed to generate a machine learning model include a support vectormachine (SVM), a naive Bayes classification, a random forest, a neuralnetwork, deep learning, or other supervised learning algorithm orunsupervised learning algorithm for classification and regression. Themachine learning algorithms may be trained using one or more trainingdatasets.

Many data analysis tasks, such as determining a fingerprint for anoperator of a vehicle or the vehicle itself, deal with data, such astelematics data, that is presented in high-dimensional spaces and the“curse of dimensionality” phenomena is often an obstacle to the use ofmany methods, including Neural Network methods, for solving these tasks.To avoid these phenomena, various representation learning algorithms maybe employed as a first key step in solutions of these tasks to transformthe original high-dimensional data into their lower-dimensionalrepresentations so that as much information as possible is preservedabout the original data required for the considered task.

As such, inferring low-dimensional representations is a common theme invarious fields, including natural language processing and networkanalysis. In the time series domain, however, existing methods attemptto capture the current state of the system itself, rather thanrepresenting the operator controlling the system. These methods areuseful in certain contexts, but they do not capture sufficient dataabout higher-level information (e.g., the operator), instead focusing onthe short-term behavior of the vehicle. In other words, these methodsencode the “actions” that a vehicle is performing (e.g., turning,braking, and so forth) instead of the “behavior” that the operator isexhibiting (e.g., aggressive, tired, and so forth). Other methods existfor operator identification, but these methods focus solely on the onespecific task, such as driver identification, clustering drivers intogroups, and so forth, and none of them encode the temporal evolution ofeach driver over the course of a drive. Instead, they all model eachdriver as having a “static” state. Furthermore, these methods are alllimited to a small number of CAN bus signals, rather than leveraging themajority of the data that is available to them.

Driver Fingerprint

The theory behind an operator’s fingerprint is that every operator hasan “algorithmic signature.” Telematics data can be used to uncover thesepatterns in behavior, such as: length of trips (e.g., long or short),type of roads driven (e.g., highway, residential roads, or back roads),and style of operating (e.g., smooth driving vs. harsh braking, suddenacceleration, fast cornering, etc.), and so forth).

Various embodiments of the described system determine a fingerprint ofan operator of a vehicle based on recorded telematics data. In someembodiments, the recorded telematics data includes time series sensordata collected from vehicles that is labeled with who is operating thevehicle at a given moment in time. In some embodiments, the describedsystem determines a low-dimensional embedding that represents thefingerprint of the operator. In some embodiments, the recordedtelematics data is matched to an operator of the vehicle based on ananalysis of the collected data and the various operator fingerprintsdetermined by the described system. In some embodiments, at every pointin time, a fingerprint embedding is returned by the described system. Insome embodiments, this embedding includes a (static) operator embeddingfor each operator, as well as a (dynamic) context shift whichcontinually varies, which shows how the operator’s state has changed dueto environmental factors. In such embodiments, this embedding includes a(static) driver embedding for each driver, as well as a (dynamic)context shift which continually varies, which shows how the driver’sstate has changed due to environmental factors. In some embodiments, thefingerprint evolves over time, based on the context. For example, a“default” fingerprint for each operator may be determined, plus a“variation” that can vary over time (e.g., tired, in traffic, and soforth). In some embodiments, the fingerprint quantifies uniquecharacteristics of each operator. ). In some embodiments, thefingerprint is similar for similar types of drivers, and different forother styles of drivers (e.g., aggressive, distracted, cautious, and soforth). In some embodiments, the fingerprint is interpretable andactionable.

The determined fingerprint describes, for example, driving style for adriver. A fingerprint may be employed to, for example, identify whethera specific person is operating a vehicle. In some embodiments,telematics data is collected by sensors in real time and can be employedto train a machine-learning model. Once trained, the machine-learningmodel may be employed by the described system to covert standardizedtelematics to a fingerprint for an operator of a vehicle. In someembodiments, a new fingerprint is generated at every timestamp in acollected telematics dataset.

Being able to accurately identify operator behaviors has profoundimplications for many industries. For example, automobile insurers wouldbe able to give better deals on a usage-based occasional driverinsurance policy. Moreover, insurers could evaluate driver behavior andtrack how often the insured vehicle is driven by the occasional driverright from the telematics data. Furthermore, the fingerprint holds greatpotential for improving driver and fleet safety by eliminating use ofvehicle by unauthorized drivers. Insurers would also be able to track ifanyone not on the insurance policy has been driving the vehicle. Anoperator’s fingerprint can also provide an opportunity for targeteddriver coaching with customized in-vehicle driver feedback or via asmartphone application.

Vehicle Fingerprint

The theory behind a vehicle’s fingerprint is that every vehicle,analogous to an operator, has an “algorithmic signature.” Telematicsdata can be used to uncover these usage patterns and technical systembehavior, such as: sensor readings from various vehicle systems,diagnostic trouble codes, maintenance triggers and records, usagepatterns (e.g., typical roads driven, operator driving styles, etc.),and so forth.

Various embodiments of the described system determine a uniquefingerprint of a vehicle based on recorded telematics and diagnosticsdata. In some embodiments, the recorded telematics data includes timeseries sensor data collected from vehicles that is labeled withdiagnostic trouble codes. In some embodiments, the described systemdetermines a low-dimensional embedding that represents the fingerprintof the vehicle. In some embodiments, the recorded telematics data ismatched to an operator of the vehicle based on an analysis of thecollected data and the various operator fingerprints determined by thedescribed system as described above. In some embodiments, at every pointin time, a vehicle fingerprint embedding is returned by the describedsystem. In some embodiments, this embedding includes a (static) vehicleembedding for each vehicle, as well as a (dynamic) context shift whichcontinually varies, which shows how the vehicle’s state has changed dueto environmental factors. In some embodiments, the vehicle fingerprintevolves over time, based on the context. For example, a “default”fingerprint for each vehicle may be determined, plus a “variation” thatcan vary over time (e.g., overheated, at risk of failure, etc.). In someembodiments, the fingerprint quantifies unique characteristics of eachvehicle. In some embodiments, the fingerprint is interpretable andactionable.

The determined vehicle fingerprint describes, for example, vehiclesystems health for a specific vehicle. A fingerprint may be employed to,for example, predict whether a specific vehicle is likely to sustain avehicle system failure. In some embodiments, telematics data iscollected by sensors in real time and can be employed to train amachine-learning model. Once trained, the machine-learning model may beemployed by the described system to convert standardized telematics to afingerprint for a vehicle. In some embodiments, a new fingerprint isgenerated at every timestamp in a collected telematics dataset.

Being able to accurately identify vehicle usage patterns and technicalsystem behaviors has profound implications for many industries. Forexample, vehicle manufacturers and fleet managers are enabled toproactively identify and repair vehicles before they sustain unexpectedfailures. Moreover, vehicle manufacturers, fleet managers, andindividual vehicle owners are enabled to more accurately assess vehiclevalue at any point in time right from the telematics data. Furthermore,the vehicle fingerprint holds great potential for vehicle manufacturersand their suppliers to improve upstream vehicle design and manufacturingdecisions to design and build safer and more reliable vehicles.

Exemplary Applications and Use Cases

FIG. 1 depicts an example process employed by the described system togenerate and update a fingerprint for an operator of a vehicle or thevehicle itself. At 100, a software module periodically receivestelematics data generated at a plurality of sensors of a vehicle. Insome embodiments, the telematics data originates at a plurality ofvehicle sensors connected to the vehicle’s CAN bus. In some embodiments,the telematics data is transmitted wirelessly via the vehicle’sconnectivity module. In some embodiments, the telematics data comprisesvehicle data. In some embodiments, the vehicle data comprises one ormore of: travel speed, wheel speed, acceleration, orientation, engineRPM, engine temperature, coolant temperature, oil temperature, currentgear, battery voltage, suspension activity, climate control systemsettings, window positions, door statuses, mirror positions, internalair temperature, tire pressures, seat belt tension, tire pressure,passenger occupancy, radar status, diagnostic trouble codes, historicalmaintenance triggers, and personalization settings. In some embodiments,the telematics data comprises environmental data. In some embodiments,the environmental data comprises one or more of: location, altitude,external air temperature, external humidity, precipitation, road type,light, and road condition. In some embodiments, the telematics datacomprises driver data. In some embodiments, the driver data comprisesone or more of: steering wheel position, steering wheel velocity, brakepedal position, braking force, gas pedal position, shifting, internallighting use, headlight use, turn signal use, mirror adjustments, windowadjustments, climate control system use, entertainment system use, andseat belt use. In some embodiments, the telematics data comprisesdemographics data. In some embodiments, the demographics data comprisesone or more of: age, gender, religion, race, income, education, andemployment. In some embodiments, at least some of the telematics data issequential time series data. In some embodiments, the telematics data isreceived at least every 15 minutes, 10 minutes, 5 minutes, 1 minute, 45seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second,including increments therein. In some embodiments, the telematics datais received substantially continuously.

At 110, a software module standardizes the telematics data.

At 120, a software module aggregates the standardized telematics data.

At 130, a software module applies a trained machine learning model toembed the aggregated telematics data into a low-dimensional state. Insome embodiments, the machine learning model comprises a neural network.In some embodiments, the neural network is a plurality of stackedrecurrent neural networks. In some embodiments, the neural networkcomprises a plurality of recurrent neural networks and a fully connectedlayer.

At 140, a software module generates a unique fingerprint for theoperator of the vehicle or the vehicle itself. In various embodiments,the fingerprint comprises a static component, a dynamic component, orboth. In some embodiments, the driver fingerprint comprises a level ofaggression. In some embodiments, the driver fingerprint comprises alevel of distraction. In some embodiments, the driver fingerprintcomprises a level of impairment. In some embodiments, the driverfingerprint comprises a level of driving risk. In some embodiments, thedriver fingerprint comprises a level of driving skill. In someembodiments, the driver fingerprint comprises a driving style. In someembodiments, the driver fingerprint comprises a driver identity. In someembodiments, the vehicle fingerprint comprises of one or more of:vehicle health score, component-specific risk score, system-aggregatedrisk score, component-specific malfunction or failure timeline,system-aggregated malfunction or failure timeline, andcomponent-specific causal pathways.

Steps 110-140 may be iteratively repeated to update the dynamiccomponent of the fingerprint. In some embodiments, steps 110-140 areiteratively repeated to update the dynamic component of the driverand/or vehicle fingerprint at least every 15 minutes, 10 minutes, 5minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5seconds, or 1 second, including increments therein. In some embodiments,steps 110-140 are iteratively repeated to update the dynamic componentof the driver and/or vehicle fingerprint substantially continuously. Insome embodiments, the example process comprises a software moduleidentifying driver and/or vehicle fingerprints from among a plurality ofdriver and/or vehicle fingerprints, which are similar to each other. Insome embodiments, the similarity is measured by a calculated similarityscore. The application further comprises a software module utilizing thedriver fingerprint to authenticate the individual in a payment system.In some embodiments, the example process comprises a software moduleutilizing the driver and/or vehicle fingerprint to authenticate theindividual in a payment system. In some embodiments, the example processcomprises a software module utilizing the driver and/or vehiclefingerprint to determine an insurance pricing factor for the individual.In some embodiments, the example process comprises a software moduleutilizing the driver and/or vehicle fingerprint to detect changes indriving behavior of the individual. In some embodiments, the exampleprocess comprises a software module utilizing the driver and/or vehiclefingerprint to personalize vehicle settings for the individual. In someembodiments, the application further comprises a software moduleidentifying vehicle fingerprints, from among a plurality of vehiclefingerprints, which are similar to each other. In various embodiments,the application further comprises a software module utilizing thevehicle fingerprint to perform one or more of: identify and groupvehicles based on likelihood of component-specific or system-aggregatedmalfunction or failure over a specific timeline, identify and groupvehicles based on common component-specific or system-aggregatedmalfunction or failure histories.

FIG. 2 depicts an example system that may be employed to generate afingerprint for an operator of a vehicle or the vehicle itself. Asdepicted, the example system includes vehicles 200, a centralizeddatabase 210, a compute cluster (which optionally comprises one or moregraphics processing units (GPUs)) 220, and an inference module 250. Thevehicles 200 may be coupled to a telematics sensor device. The vehicles200 communicate the collected telematics data through the CAN bus via anetwork (not shown) to a backend system that includes a centralizeddatabase 210. The telematics data is employed to train amachine-learning model 230 using the compute cluster 220, whichcomprises, in some embodiments, a GPU cluster. Once the machine-learningmodel 230 has been trained, the inference module 240 processes thereceived telematics data through the trained machine-learning model 230to generate a fingerprint for the operators of each of the vehicles 200or the vehicles themselves. Model weights from the trainedmachine-learning module 230 can be employed by the inference module 250to continuously generate/update the fingerprints for the operatorsand/or vehicles as additional telematics data is collected for therespective vehicle sensors. In some embodiments, the inference module250 is deployed to the vehicles 200, such as depicted. In someembodiments, the inference module 250 is deployed via a backend system,such as a cloud-based, to which the vehicle communicates via, forexample, a network (see FIG. 5 ).

FIG. 3 depicts an example system that may be employed to generate afingerprint 330 for an operator of a vehicle or the vehicle itself. Asdepicted, sequential CAN bus readings over k timestamps 300 (thetelematics data) is fed into multiple stacked LSTM layers of therecurrent neural network 310 to reduce temporal variations. Then, theoutput of the last LSTM layer is fed to a few fully connected layers,which dimensionally reduces and transform the features into a space thatmakes that output, the driver fingerprint 330, easier to classify.

FIG. 4 depicts an example system to generate and update a fingerprintfor an operator of a vehicle or the vehicle itself. The depicted systemincludes auto-encoder 410, dynamic system model 420, and the output ofthe depicted system, the operator and/or vehicle fingerprint 430. Insome embodiments, the auto-encoder 410 is a type of artificial neuralnetwork used to learn efficient data codings in an unsupervised manner.In some embodiments, the auto-encoder 410 learns a representation(encoding) for a set of data (e.g., the time series data), fordimensionality reduction, by training the network to ignore signal“noise.” Along with the reduction side, a reconstructing side is learnt,where the auto-encoder 410 generates a representation as close aspossible to its original input from the reduced encoding. The dynamicsystem model 420 is a machine-learning model, such as machine-learningmodel 230, trained with telematics data. As depicted, the dynamic systemmodel 420 receives the low dimension embedding of the time-series datafrom the auto-encoder 410. The models’ weights provide the output of thesystem, which is the operator and/or vehicle fingerprint 430.

FIG. 5 depicts an example environment 500 that can be employed toexecute implementations of the present disclosure. As depicted, theexample environment 500 includes the vehicles 200; a back-end system520; and a network 510. Vehicles 502, 504, and 506 are substantiallysimilar to vehicle 200 depicted in FIG. 2 and each may employ theinference module 250, which can be deployed directly to the vehicles oraccessed on the backend system 520 via the network 510. Three vehiclesare depicted in FIG. 5 ; however, such an environment may be implementedwith any number of deployed vehicles. Also, as depicted, the inferencemodule 250 is deployed directly to vehicle 504, while vehicles 502 and506 access the inference module 250 through the backend system 520 viathe network 510. Other possible implementations are contemplated.

In some embodiments, the network 510 includes a local area network(LAN), wide area network (WAN), the Internet, or a combination thereof,and connects web sites, devices (e.g., vehicles 502, 504, and 506) andback-end systems (e.g., the back-end system 520). The network 510 can bethe Internet, an internet and/or extranet, or an intranet and/orextranet that is in communication with the Internet. The network 520 insome cases is a telecommunication and/or data network. In someembodiments, the network 510 may be accessed over a wired and/or awireless communications link. For example, each of the vehicles 502,504, and 506 may employ a mobile communication device to access thenetwork 510 through, for example, a cellular network.

In some embodiments, the back-end system 520 includes server-classhardware type devices. In some embodiments, the back-end system 520includes computer systems using clustered computers and components toact as a single pool of seamless resources when accessed through thenetwork 510. For example, such embodiments may be used in data center,cloud computing, storage area network (SAN), and network attachedstorage (NAS) applications. In some embodiments, back-end system theinference module 250 is deployed using containerization.

In the depicted example environment 500, the back-end system 520includes at least one server system 522 and a least one data store 524.In some embodiments, the at least one server system 522 hosts one ormore computer-implemented services through which the vehicles 502, 504,and 506 may send and receive data. For example, in some embodiments, thevehicles may each provide collected telematics data through a CAN bus toand receive model weights for an inference model 250 deployed to thevehicle from the backend-system 520, such as described previously. Insuch embodiments, the back-end system 520 may generated an inferencemodel and/or model weights with a trained machine-learning model, suchas described previously. In some embodiments, the back-end system 520provides an application programming interface (API) services with whicheach of vehicles 502, 504, and 506 may communicate.

Digital Processing Device

In some embodiments, the platforms, systems, media, and methodsdescribed herein include a digital processing device, or use of thesame. In further embodiments, the digital processing device includes oneor more hardware central processing units (CPUs) or general purposegraphics processing units (GPGPUs) that carry out the device’sfunctions. In still further embodiments, the digital processing devicefurther comprises an operating system configured to perform executableinstructions. In some embodiments, the digital processing device isoptionally connected a computer network. In further embodiments, thedigital processing device is optionally connected to the Internet suchthat it accesses the World Wide Web. In still further embodiments, thedigital processing device is optionally connected to a cloud computinginfrastructure. In other embodiments, the digital processing device isoptionally connected to an intranet. In other embodiments, the digitalprocessing device is optionally connected to a data storage device.

In accordance with the description herein, suitable digital processingdevices include, by way of non-limiting examples, server computers,desktop computers, laptop computers, notebook computers, sub-notebookcomputers, netbook computers, netpad computers, set-top computers, mediastreaming devices, handheld computers, Internet appliances, mobilesmartphones, tablet computers, personal digital assistants, video gameconsoles, and vehicles. Those of skill in the art will recognize thatmany smartphones are suitable for use in the system described herein.Those of skill in the art will also recognize that select televisions,video players, and digital music players with optional computer networkconnectivity are suitable for use in the system described herein.Suitable tablet computers include those with booklet, slate, andconvertible configurations, known to those of skill in the art.

In some embodiments, the digital processing device includes an operatingsystem configured to perform executable instructions. The operatingsystem is, for example, software, including programs and data, whichmanages the device’s hardware and provides services for execution ofapplications. Those of skill in the art will recognize that suitableserver operating systems include, by way of non-limiting examples,FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle®Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in theart will recognize that suitable personal computer operating systemsinclude, by way of non-limiting examples, Microsoft® Windows®, Apple®Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. Insome embodiments, the operating system is provided by cloud computing.

In some embodiments, the device includes a storage and/or memory device.The storage and/or memory device is one or more physical apparatusesused to store data or programs on a temporary or permanent basis. Insome embodiments, the device is volatile memory and requires power tomaintain stored information. In some embodiments, the device isnon-volatile memory and retains stored information when the digitalprocessing device is not powered. In further embodiments, thenon-volatile memory comprises flash memory. In some embodiments, thenon-volatile memory comprises dynamic random-access memory (DRAM). Insome embodiments, the non-volatile memory comprises ferroelectric randomaccess memory (FRAM). In some embodiments, the non-volatile memorycomprises phase-change random access memory (PRAM). In otherembodiments, the device is a storage device including, by way ofnon-limiting examples, CD-ROMs, DVDs, flash memory devices, magneticdisk drives, magnetic tapes drives, optical disk drives, and cloudcomputing based storage. In further embodiments, the storage and/ormemory device is a combination of devices such as those disclosedherein.

In some embodiments, the digital processing device includes a display tosend visual information to a user. In some embodiments, the display is acathode ray tube (CRT). In some embodiments, the display is a liquidcrystal display (LCD). In further embodiments, the display is a thinfilm transistor liquid crystal display (TFT-LCD). In some embodiments,the display is an organic light emitting diode (OLED) display. Invarious further embodiments, on OLED display is a passive-matrix OLED(PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments,the display is a plasma display. In other embodiments, the display is avideo projector. In yet other embodiments, the display is a vehicleheads-up display (HUD) in communication with the digital processingdevice.

In some embodiments, the digital processing device includes an inputdevice to receive information from a user. In some embodiments, theinput device is a keyboard. In some embodiments, the input device is apointing device including, by way of non-limiting examples, a mouse,trackball, track pad, joystick, game controller, or stylus. In someembodiments, the input device is a touch screen or a multi-touch screen.In other embodiments, the input device is a microphone to capture voiceor other sound input. In other embodiments, the input device is a videocamera or other sensor to capture motion or visual input. In furtherembodiments, the input device is a Kinect, Leap Motion, or the like. Instill further embodiments, the input device is a combination of devicessuch as those disclosed herein.

Referring to FIG. 6 , in a particular embodiment, an exemplary digitalprocessing device 601 is programmed or otherwise configured to collect,transmit, receive or process telematics data and/or utilize driverfingerprints. In this embodiment, the digital processing device 601includes a central processing unit (CPU, also “processor” and “computerprocessor” herein) 605, which can be a single core or multi coreprocessor, or a plurality of processors for parallel processing. Thedigital processing device 601 also includes memory or memory location610 (e.g., random-access memory, read-only memory, flash memory),electronic storage unit 615 (e.g., hard disk), communication interface620 (e.g., network adapter) for communicating with one or more othersystems, and peripheral devices 625, such as cache, other memory, datastorage and/or electronic display adapters. The memory 610, storage unit615, interface 620 and peripheral devices 625 are in communication withthe CPU 605 through a communication bus (solid lines), such as amotherboard. The storage unit 615 can be a data storage unit (or datarepository) for storing data. The digital processing device 601 can beoperatively coupled to the computer network 510 with the aid of thecommunication interface 620. The network 510, in some cases with the aidof the device 601, can implement a peer-to-peer network, which mayenable devices coupled to the device 601 to behave as a client or aserver.

Continuing to refer to FIG. 6 , the CPU 605 can execute a sequence ofmachine-readable instructions, which can be embodied in a program orsoftware. The instructions may be stored in a memory location, such asthe memory 610. The instructions can be directed to the CPU 605, whichcan subsequently program or otherwise configure the CPU 605 to implementmethods of the present disclosure. Examples of operations performed bythe CPU 605 can include fetch, decode, execute, and write back. The CPU605 can be part of a circuit, such as an integrated circuit. One or moreother components of the device 601 can be included in the circuit. Insome cases, the circuit is an application specific integrated circuit(ASIC) or a field programmable gate array (FPGA).

Continuing to refer to FIG. 6 , the storage unit 615 can store files,such as drivers, libraries and saved programs. The storage unit 615 canstore user data, e.g., user preferences and user programs. The digitalprocessing device 601 in some cases can include one or more additionaldata storage units that are external, such as located on a remote serverthat is in communication through an intranet or the Internet.

Continuing to refer to FIG. 6 , the digital processing device 601 cancommunicate with one or more remote computer systems through the network510. For instance, the device 601 can communicate with a remote computersystem of a user. Examples of remote computer systems include servers,personal computers (e.g., portable PC), slate or tablet computers (e.g.,Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple®iPhone, Android-enabled device, Blackberry®), or personal digitalassistants.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the digital processing device 601, such as, for example, onthe memory 610 or electronic storage unit 615. The machine executable ormachine readable code can be provided in the form of software. Duringuse, the code can be executed by the processor 605. In some cases, thecode can be retrieved from the storage unit 615 and stored on the memory610 for ready access by the processor 605. In some situations, theelectronic storage unit 615 can be precluded, and machine-executableinstructions are stored on memory 610.

Non-Transitory Computer Readable Storage Medium

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more non-transitory computer readablestorage media encoded with a program including instructions executableby the operating system of an optionally networked digital processingdevice. In further embodiments, a computer readable storage medium is atangible component of a digital processing device. In still furtherembodiments, a computer readable storage medium is optionally removablefrom a digital processing device. In some embodiments, a computerreadable storage medium includes, by way of non-limiting examples,CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic diskdrives, magnetic tape drives, optical disk drives, distributed computingsystems including cloud computing systems and services, and the like. Insome cases, the program and instructions are permanently, substantiallypermanently, semi-permanently, or non-transitorily encoded on the media.

Computer Program

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include at least one computer program, or use of thesame. A computer program includes a sequence of instructions, executablein the digital processing device’s CPU, written to perform a specifiedtask. Computer readable instructions may be implemented as programmodules, such as functions, objects, Application Programming Interfaces(APIs), data structures, and the like, that perform particular tasks orimplement particular abstract data types. In light of the disclosureprovided herein, those of skill in the art will recognize that acomputer program may be written in various versions of variouslanguages.

The functionality of the computer readable instructions may be combinedor distributed as desired in various environments. In some embodiments,a computer program comprises one sequence of instructions. In someembodiments, a computer program comprises a plurality of sequences ofinstructions. In some embodiments, a computer program is provided fromone location. In other embodiments, a computer program is provided froma plurality of locations. In various embodiments, a computer programincludes one or more software modules. In various embodiments, acomputer program includes, in part or in whole, one or more webapplications, one or more mobile applications, one or more standaloneapplications, one or more web browser plug-ins, extensions, add-ins, oradd-ons, or combinations thereof.

Web Application

In some embodiments, a computer program includes a web application. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a web application, in various embodiments, utilizes oneor more software frameworks and one or more database systems. In someembodiments, a web application is created upon a software framework suchas Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a webapplication utilizes one or more database systems including, by way ofnon-limiting examples, relational, non-relational, object oriented,associative, and XML database systems. In further embodiments, suitablerelational database systems include, by way of non-limiting examples,Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the artwill also recognize that a web application, in various embodiments, iswritten in one or more versions of one or more languages. A webapplication may be written in one or more markup languages, presentationdefinition languages, client-side scripting languages, server-sidecoding languages, database query languages, or combinations thereof. Insome embodiments, a web application is written to some extent in amarkup language such as Hypertext Markup Language (HTML), ExtensibleHypertext Markup Language (XHTML), or eXtensible Markup Language (XML).In some embodiments, a web application is written to some extent in apresentation definition language such as Cascading Style Sheets (CSS).In some embodiments, a web application is written to some extent in aclient-side scripting language such as Asynchronous JavaScript and XML(AJAX), Flash® ActionScript, JavaScript, or Silverlight®. In someembodiments, a web application is written to some extent in aserver-side coding language such as Active Server Pages (ASP),ColdFusion®, Perl, Java™, Scala, Go, JavaServer Pages (JSP), HypertextPreprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy.In some embodiments, a web application is written to some extent in adatabase query language such as Structured Query Language (SQL). In someembodiments, a web application integrates enterprise server productssuch as IBM® Lotus Domino®. In some embodiments, a web applicationincludes a media player element. In various further embodiments, a mediaplayer element utilizes one or more of many suitable multimediatechnologies including, by way of non-limiting examples, Adobe® Flash®,HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile applicationprovided to a mobile digital processing device. In some embodiments, themobile application is provided to a mobile digital processing device atthe time it is manufactured. In other embodiments, the mobileapplication is provided to a mobile digital processing device via thecomputer network described herein.

In view of the disclosure provided herein, a mobile application iscreated by techniques known to those of skill in the art using hardware,languages, and development environments known to the art. Those of skillin the art will recognize that mobile applications are written inseveral languages. Suitable programming languages include, by way ofnon-limiting examples, C, C++, C#, Objective-C, Java™, Scala, Go,JavaScript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, andXHTML/HTML with or without CSS, or combinations thereof.

Suitable mobile application development environments are available fromseveral sources. Commercially available development environmentsinclude, by way of non-limiting examples, AirplaySDK, alcheMo,Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework,Rhomobile, and WorkLight Mobile Platform. Other development environmentsare available without cost including, by way of non-limiting examples,Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile devicemanufacturers distribute software developer kits including, by way ofnon-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK,BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, andWindows® Mobile SDK.

Those of skill in the art will recognize that several commercial forumsare available for distribution of mobile applications including, by wayof non-limiting examples, Apple® App Store, Google® Play, ChromeWebStore, BlackBerry® App World, App Store for Palm devices, App Catalogfor webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia®devices, Samsung® Apps, and Nintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Scala, Go,Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic,and VB .NET, or combinations thereof. Compilation is often performed, atleast in part, to create an executable program. In some embodiments, acomputer program includes one or more executable complied applications.

Software Modules

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include software, server, and/or database modules, oruse of the same. In view of the disclosure provided herein, softwaremodules are created by techniques known to those of skill in the artusing machines, software, and languages known to the art. The softwaremodules disclosed herein are implemented in a multitude of ways. Invarious embodiments, a software module comprises a file, a section ofcode, a programming object, a programming structure, or combinationsthereof. In further various embodiments, a software module comprises aplurality of files, a plurality of sections of code, a plurality ofprogramming objects, a plurality of programming structures, orcombinations thereof. In various embodiments, the one or more softwaremodules comprise, by way of non-limiting examples, a web application, amobile application, and a standalone application. In some embodiments,software modules are in one computer program or application. In otherembodiments, software modules are in more than one computer program orapplication. In some embodiments, software modules are hosted on onemachine. In other embodiments, software modules are hosted on more thanone machine. In further embodiments, software modules are hosted oncloud computing platforms. In some embodiments, software modules arehosted on one or more machines in one location. In other embodiments,software modules are hosted on one or more machines in more than onelocation.

Databases

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more databases, or use of the same. Inview of the disclosure provided herein, those of skill in the art willrecognize that many databases are suitable for storage and retrieval oftelematics data, machine learning model weights, driver fingerprintinformation, and the like. In various embodiments, suitable databasesinclude, by way of non-limiting examples, relational databases,non-relational databases, object oriented databases, object databases,entity-relationship model databases, associative databases, and XMLdatabases. Further non-limiting examples include SQL, PostgreSQL, MySQL,Oracle, DB2, and Sybase. In some embodiments, a database isinternet-based. In further embodiments, a database is web-based. Instill further embodiments, a database is cloud computing-based. In otherembodiments, a database is based on one or more local computer storagedevices.

While preferred embodiments of the present subject matter have beenshown and described herein, it will be obvious to those skilled in theart that such embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the subject matter described herein.It should be understood that various alternatives to the embodiments ofthe subject matter described herein may be employed.

What is claimed is:
 1. A computer-implemented system comprising: adigital processing device comprising: at least one processor, anoperating system configured to perform executable instructions, amemory, and a computer program including instructions executable by thedigital processing device to create an application applying machinelearning to telematics data to generate a unique vehicle fingerprint fora specific vehicle, the application comprising: a) a software moduleperiodically receiving telematics data generated at a plurality ofsensors of a vehicle; b) a software module standardizing the telematicsdata; c) a software module aggregating the standardized telematics data;d) a software module applying a trained machine learning model to embedthe aggregated telematics data into a low-dimensional state; and e) asoftware module generating a unique vehicle fingerprint for the specificvehicle, the vehicle fingerprint comprising a static component, adynamic component, or both a static component and a dynamic component;wherein b) - e) are iteratively repeated to update the dynamic componentof the vehicle fingerprint.
 2. The system of claim 1, wherein thetelematics data originates at a plurality of vehicle sensors connectedto the vehicle’s controller area network (CAN) bus.
 3. The system ofclaim 1, wherein the telematics data is transmitted wirelessly via thevehicle’s connectivity module.
 4. The system of claim 1, wherein thetelematics data comprises vehicle data.
 5. The system of claim 4,wherein the vehicle data comprises one or more of: travel speed, wheelspeed, acceleration, orientation, engine revolutions per minute (RPM),engine temperature, coolant temperature, oil temperature, current gear,battery voltage, suspension activity, climate control system settings,window positions, door statuses, mirror positions, internal airtemperature, tire pressures, seat belt tension, tire pressure, passengeroccupancy, radar status, diagnostic trouble codes, historicalmaintenance triggers, and personalization settings.
 6. The system ofclaim 1, wherein the telematics data comprises environmental data. 7.The system of claim 6, wherein the environmental data comprises one ormore of: location, altitude, external air temperature, externalhumidity, precipitation, road type, light, and road condition.
 8. Thesystem of claim 1, wherein the telematics data comprises driver data. 9.The system of claim 8, wherein the driver data comprises one or more of:steering wheel position, steering wheel velocity, brake pedal position,braking force, gas pedal position, shifting, internal lighting use,headlight use, turn signal use, mirror adjustments, window adjustments,climate control system use, entertainment system use, and seat belt use.10. The system of claim 1, wherein at least some of the telematics datais sequential time series data.
 11. The system of claim 1, wherein thetelematics data is received at least every 15 minutes, 10 minutes, 5minutes, 1 minute, 45 seconds, 30 seconds, 15 seconds, 10 seconds, 5seconds, or 1 second.
 12. The system of claim 11, wherein the telematicsdata is received substantially continuously.
 13. The system of claim 1,wherein the machine learning model comprises a neural network.
 14. Thesystem of claim 13, wherein the neural network is a plurality of stackedrecurrent neural networks.
 15. The system of claim 13, wherein theneural network comprises a plurality of recurrent neural networks and afully connected layer.
 16. The system of claim 1, wherein b) - e) areiteratively repeated to update the dynamic component of the vehiclefingerprint at least every 15 minutes, 10 minutes, 5 minutes, 1 minute,45 seconds, 30 seconds, 15 seconds, 10 seconds, 5 seconds, or 1 second.17. The system of claim 16, wherein b) - e) are iteratively repeated toupdate the dynamic component of the vehicle fingerprint substantiallycontinuously.
 18. The system of claim 1, wherein the vehicle fingerprintcomprises a component malfunction or vehicle system failure risk. 19.The system of claim 1, wherein the vehicle fingerprint comprises asystem-aggregated malfunction or failure risk.
 20. The system of claim1, wherein the application further comprises a software moduleidentifying vehicle fingerprints, from among a plurality of vehiclefingerprints, which are similar to each other.
 21. The system of claim20, wherein the similarity is measured by a calculated similarity score.22. The system of claim 1, wherein the application further comprises asoftware module utilizing the vehicle fingerprint to predict futurecomponent-specific or vehicle-system-specific malfunctions or failures.23. The system of claim 1, wherein the application further comprises asoftware module utilizing the vehicle fingerprint to predict futuresystem-aggregated malfunctions or failures.
 24. The system of claim 1,wherein the application further comprises a software module utilizingthe vehicle fingerprint to identify and group vehicles based on commoncomponent-specific or system-aggregated malfunction or failurehistories.
 25. A computer-implemented method of generating a uniquevehicle fingerprint for a specific vehicle comprising: a) periodicallycollecting, by a computer, telematics data generated at a plurality ofsensors of a vehicle; b) standardizing, by the computer, the telematicsdata; c) training, at a computer cluster, a machine learning model toembed the aggregated telematics data into a low-dimensional state; d)applying, by the computer or a vehicle, the trained machine learningmodel to embed the aggregated telematics data into a low-dimensionalstate; e) generating, by the computer or the vehicle, a unique vehiclefingerprint, the vehicle fingerprint comprising a static component, adynamic component, or both a static component and a dynamic component;and f) iteratively repeating steps b) - e) to update the dynamiccomponent of the vehicle fingerprint.
 26. The method of claim 25,wherein the method further comprises: a) saving, by the computer or thevehicle, weights generated by the trained machine leaning model; and b)inferring, by the computer or the vehicle, a unique vehicle fingerprintfor the specific vehicle based on the weights for novel telematics datagenerated at a plurality of sensors of the vehicle.
 27. A system forapplying machine learning to telematics data to generate a uniquevehicle fingerprint for a specific vehicle comprising: a) at least oneserver processor configured to perform at least the following: i)periodically receive telematics data generated at a plurality of sensorsof a vehicle; ii) standardize the telematics data; iii) aggregate thestandardized telematics data; iv) apply a trained machine learning modelto embed the aggregated telematics data into a low-dimensional state; v)save weights generated by the trained machine leaning model; vi)transmit the saved weights to the vehicle; and vii) iterativelyrepeating a) ii) - a) vi) to update the transmitted weights; and b) atleast one vehicle processor configured to perform at least thefollowing: i) receive the transmitted weights; and ii) infer a uniquevehicle fingerprint for the specific vehicle based on the transmittedweights for novel telematics data generated at a plurality of sensors ofthe vehicle, the vehicle fingerprint comprising a static component, adynamic component, or both a static component and a dynamic component.